A power distribution room monitoring data transmission method and system

Abstract

The application provides a power distribution room monitoring data transmission method and system, and belongs to the technical field of industrial internet. The method comprises the following steps: an edge gateway acquires local collected power data; according to a preset differentiated transmission strategy, the power data is classified and processed locally, and the processed data is uploaded to a cloud server through a public wireless communication network, so that the cloud server receives and processes the data; the differentiated transmission strategy comprises the following steps: a first transmission rule is adopted for data in a normal operating state, a second transmission rule is adopted for event-triggered related data, and a third transmission rule is adopted for cached data during network interruption. Based on the differentiated transmission strategy and the edge autonomous mechanism, real-time dependence on the cloud server can be reduced, and the traffic consumption and bandwidth pressure of the transmission network can be effectively reduced.

Description

Technical Field

[0001] This application relates to the field of industrial internet technology, specifically to a method and system for data transmission in power distribution room monitoring. Background Technology

[0002] In the field of power system distribution automation, the substation, as a key node at the end of the distribution network, plays a crucial role in ensuring power supply reliability through real-time monitoring of its operational status. Currently, large power companies such as the State Grid have established relatively complete substation monitoring systems, typically employing dedicated power communication networks and adhering to international standards such as IEC 60870-5-101 / 104 and IEC 61850 for data transmission. These dedicated network solutions rely on the power system's own infrastructure and feature high bandwidth, low latency, and high reliability, meeting stringent security and real-time requirements. However, this approach is highly dependent on investment in dedicated network resources, resulting in high construction and maintenance costs, and is generally only applicable to core substations directly managed by the power grid company.

[0003] In practice, at the end of the power system, there are numerous substations managed by industrial parks, commercial buildings, residential communities, factories, and mines. These substations are numerous and widely distributed, yet they also bear the important responsibility of ensuring power supply, making the need for remote monitoring of their operational status increasingly urgent. To address the intelligent operation and maintenance needs of these decentralized substations, professional third-party operation and maintenance service companies have emerged in the market, aiming to provide a one-stop service including remote monitoring, fault early warning, and operation and maintenance for numerous user-side substations in a centralized manner.

[0004] During the implementation of actual monitoring projects, it was found that the power distribution rooms of the inventors generally lacked the conditions to connect to a dedicated power network. Limited by investment costs and infrastructure, it was difficult to adopt traditional dedicated network monitoring solutions. Utilizing public wireless networks (such as 4G networks) to achieve remote data transmission has become a realistic technical approach to meet the monitoring needs of such power distribution rooms.

[0005] However, applying 4G public networks to power distribution room monitoring scenarios presents significantly different technical challenges compared to private network environments. 4G networks have limited bandwidth resources and incur traffic billing costs, and the stability of network signals in power distribution rooms is difficult to guarantee. Directly adopting mature data transmission models from private network environments (such as high-frequency full data uploads and centralized cloud processing) would lead to uncontrollable traffic costs, increased network congestion risks, and a lack of autonomous processing capabilities at the edge during network outages, posing a risk of critical data loss. Furthermore, power distribution room monitoring involves a large number of data points, and accurate analysis of abnormal events often requires multi-dimensional data support. Existing general data transmission solutions lack effective mechanisms for organizing and transmitting such related data, impacting the actual effectiveness of remote monitoring.

[0006] To address these challenges, the industry has generally attempted to apply edge-cloud collaborative architectures. However, when this general architecture is directly applied to 4G monitoring of large-scale power distribution room clusters, the unique characteristics of the power distribution room scenario reveal numerous incompatibilities in practical applications. Power distribution rooms are located in complex environments, making it difficult to guarantee the stability of 4G network signals; power systems have high requirements for the timeliness of response to abnormal events; and accurate fault diagnosis often requires the collaborative support of multi-dimensional data. Given these scenario characteristics, existing edge-cloud collaborative transmission solutions designed for general industrial scenarios face numerous challenges in practical applications.

[0007] Therefore, how to build an economical, reliable, and intelligent remote monitoring system for power distribution rooms using public networks such as 4G when access to the dedicated power grid is not possible has become a technical problem that third-party operation and maintenance service companies urgently need to solve in their actual business operations. Summary of the Invention

[0008] To at least partially overcome the problems existing in related technologies, this application proposes a data transmission method and system for power distribution room monitoring. Based on differentiated transmission strategies and edge autonomy mechanisms, it helps to reduce data transmission load, improve network interruption recovery capabilities, and enhance event analysis efficiency while ensuring the real-time performance and reliability of monitoring.

[0009] First aspect This application provides a power distribution room monitoring data transmission method, applied to an edge gateway, characterized in that the power distribution room monitoring data transmission method includes: Acquire locally collected power data; According to a preset differentiated transmission strategy, the power data is classified and processed locally, and the processed data is uploaded to the cloud server through a public wireless communication network so that the cloud server can receive and process the data. The differentiated transmission strategy includes: using a first transmission rule for data under normal operating conditions, using a second transmission rule for data related to events, and using a third transmission rule for data cached during network outages.

[0010] In some possible implementations, the second transmission rule includes: When a preset event is detected, core event information is generated and uploaded to the cloud server through a preset second transmission channel; The auxiliary data and media data associated with the preset event are acquired, and the auxiliary data and media data are uploaded to the cloud server through the preset third and fourth transmission channels.

[0011] In some possible implementations, the core event information includes at least the event type, device number, event threshold, and current measured value; the auxiliary data includes change characteristic data or sampling data of the event device within a preset time period; and the media data includes video clips or comparison images before and after the event.

[0012] In some possible implementations, the first transmission rule includes: compressing the acquired full power data according to a preset period to generate a periodic full data packet; The third transmission rule includes: when a network interruption is detected, the acquired power data and event-related data are cached in local storage; when the network is restored, the cached data is read from the local storage and uploaded to the cloud server in batches through the preset fifth transmission channel; and the uploaded cached data is cleared after receiving confirmation from the cloud server.

[0013] In some possible implementations, the data packets uploaded in batches include a resume batch number and a current batch sequence number, so that the cloud server can reassemble the received data based on the resume batch number and the current batch sequence number.

[0014] In some possible implementations, the first, second, third, fourth, and fifth transmission channels are predefined message topics based on the MQTT protocol; wherein, the edge gateway uses its own unique code as an MQTT client identifier to access the cloud MQTT server, the message topics are divided into multiple secondary topics according to business type, and the edge gateway realizes the uploading of different types of data by publishing messages to the corresponding secondary topics.

[0015] In some possible implementations, uploading the processed data to the cloud server via a public wireless communication network includes: The edge gateway uses its own unique code as an identifier to access the cloud server; The uploaded data is encoded in binary serialization format, and the header of the data packet contains at least the edge gateway's unique code and a timestamp.

[0016] In some possible implementations, the public wireless communication network includes a 4G network.

[0017] Second aspect This application provides a power distribution room monitoring data transmission system, the system comprising: The end-side acquisition layer, deployed in the power distribution room, is used to collect raw power parameter data; An edge processing layer includes edge gateways deployed in each power distribution room, the edge gateways being used to execute the power distribution room monitoring data transmission method described in any of the implementations of the first aspect above; The transmission link layer provides a wireless network connection based on a public wireless communication network for data transmission between the edge processing layer and the cloud service layer; The cloud service layer, deployed in the cloud, is used to receive and process data uploaded by the edge gateway.

[0018] In some possible implementations, the edge processing layer also integrates an edge AI inference module for performing event recognition locally; the cloud service layer also integrates an event auxiliary discrimination module for verifying events by combining auxiliary data and media data uploaded from the edge side, and then sending the event recognition threshold to the edge gateway after optimizing the event recognition threshold based on global data.

[0019] The technical solution proposed in this application addresses the specific needs of the application's technical scenario by employing a differentiated transmission strategy and an edge autonomy mechanism to help reduce data transmission load. Specifically, this application uses an edge gateway to classify and process power data locally and uploads the data to the cloud server according to a preset differentiated transmission strategy. This allows data in different states to be processed using differentiated transmission rules, thus avoiding high-frequency, indiscriminate uploading of all data during normal operation and effectively reducing network traffic consumption and bandwidth pressure. Simultaneously, because the edge gateway completes data classification and processing locally, event-triggered data can be initially identified and processed locally, reducing real-time dependence on the cloud server. A third transmission rule is used for data during network outages, enabling the edge gateway to cache data locally during network interruptions and upload it after network recovery, ensuring data integrity and business continuity under network fluctuation scenarios. Furthermore, the differentiated transmission strategy uses different transmission rules for normal operation data and event-triggered data, allowing the two types of data to be transmitted according to their respective characteristics, providing a foundation for subsequent data processing by the cloud server. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a power distribution room monitoring data transmission method according to an embodiment of this application. Figure 2 This is a schematic diagram of a power distribution room monitoring data transmission system provided in one embodiment of this application. Detailed Implementation

[0021] To make the purpose, technical solution and advantages of this application clearer, the technical solution of this application will be described in detail below.

[0022] As described in the background section, in practice, at the end of the power system, there are numerous substations managed by industrial parks, commercial buildings, residential communities, factories, and mines. These substations are numerous and widely distributed, yet they also bear important responsibilities for power supply security, making the need for remote monitoring of their operational status increasingly urgent. To address the intelligent operation and maintenance needs of these decentralized substations, professional third-party operation and maintenance service companies have emerged in the market, aiming to provide a one-stop service including remote monitoring, fault early warning, and operation and maintenance for numerous user-side substations in an intensive manner.

[0023] During the implementation of actual monitoring projects, it was found that the power distribution rooms of the inventors generally lacked the conditions to connect to a dedicated power network. Limited by investment costs and infrastructure, it was difficult to adopt traditional dedicated network monitoring solutions. Utilizing public wireless networks (such as 4G networks) to achieve remote data transmission has become a realistic technical approach to meet the monitoring needs of such power distribution rooms.

[0024] However, applying 4G public networks to power distribution room monitoring scenarios presents significantly different technical challenges compared to private network environments. 4G networks have limited bandwidth resources and incur traffic billing costs, and the stability of network signals in power distribution rooms is difficult to guarantee. Directly adopting mature data transmission models from private network environments (such as high-frequency full data uploads and centralized cloud processing) would lead to uncontrollable traffic costs, increased network congestion risks, and a lack of autonomous processing capabilities at the edge during network outages, posing a risk of critical data loss. Furthermore, power distribution room monitoring involves a large number of data points, and accurate analysis of abnormal events often requires multi-dimensional data support. Existing general data transmission solutions lack effective mechanisms for organizing and transmitting such related data, impacting the actual effectiveness of remote monitoring.

[0025] To address these challenges, the industry has generally attempted to apply edge-cloud collaborative architectures. However, when this general architecture is directly applied to 4G monitoring of large-scale power distribution room clusters, the unique characteristics of the power distribution room scenario reveal numerous incompatibilities in practical applications. Power distribution rooms are located in complex environments, making it difficult to guarantee the stability of 4G network signals; power systems have high requirements for the timeliness of response to abnormal events; and accurate fault diagnosis often requires the collaborative support of multi-dimensional data. Given these scenario characteristics, existing edge-cloud collaborative transmission solutions designed for general industrial scenarios face numerous challenges in practical applications.

[0026] Based on this, in order to overcome the problems existing in related technologies to a certain extent, this application proposes a data transmission method for power distribution room monitoring, which is based on differentiated transmission strategy and edge autonomy mechanism, so as to help reduce data transmission load and enhance event analysis efficiency while ensuring the real-time performance and reliability of monitoring.

[0027] In one embodiment, based on the background technology, the remote monitoring of the power distribution room adopts an edge-cloud collaborative architecture. For example, for 1,000 power distribution rooms, one edge gateway is deployed in each power distribution room, each gateway collects 2,000 power data points, and a cloud server is deployed in the cloud. Communication and interaction between each edge gateway and the cloud server are realized through a public wireless communication network such as 4G network.

[0028] like Figure 1 As shown, in this embodiment, the power distribution room monitoring data transmission method proposed in this application is applied to an edge gateway, and includes: Step S110: Obtain locally collected power data; For example, the edge gateway here can establish communication connections with various smart sensors, protection devices, and smart meters deployed in the power distribution room via industrial bus or Ethernet. These data acquisition devices include, but are not limited to, voltage transformers, current transformers, temperature and humidity sensors, and switch quantity acquisition modules, for real-time monitoring of the power distribution room's operating status. The edge gateway sends read commands to each data acquisition device according to a preset acquisition cycle (e.g., 1 second or 1 minute), or receives data actively reported by the data acquisition devices, to acquire power data including three-phase voltage, three-phase current, active power, reactive power, power factor, switch status, temperature, and humidity. Taking 1000 power distribution rooms and 2000 data points per room as an example, each edge gateway is responsible for data interaction with the 2000 data acquisition points in its own power distribution room, and organizes the acquired raw data into an internal data structure according to fields such as data point number, acquisition time, and value for subsequent processing.

[0029] Based on step S110, step S120 proceeds to classify and process the power data locally according to a preset differentiated transmission strategy. The processed data is then uploaded to the cloud server via a public wireless communication network, allowing the cloud server to receive and process the data. For example, after receiving the data uploaded by the edge gateway, the cloud server performs corresponding processing based on the data type and source, including data storage, event logging, and providing monitoring information to maintenance personnel. It should be noted that in this embodiment, the differentiated transmission strategy includes: a first transmission rule for data under normal operating conditions, a second transmission rule for event-triggered data, and a third transmission rule for data cached during network outages.

[0030] In practical implementation, after acquiring locally collected power data, the edge gateway first classifies and processes the data locally. For example, the edge gateway can determine the current data's state type based on preset discrimination conditions: if the values ​​of all data points are within the preset normal range and there is no state change, it is determined to be in normal operation; if any data point is detected to have a value change or exceed the preset range, it is determined to be in an event-triggered state. That is, this application builds an independent local autonomous capability on the edge gateway side, meaning that the edge gateway can analyze the collected power data locally in real time and independently complete change detection and limit violation detection.

[0031] In some embodiments, the first transmission rule includes: compressing the acquired full power data according to a preset period to generate a periodic full data packet; and uploading the periodic full data packet to the cloud server through a preset first transmission channel. That is, for data under normal operating conditions, the edge gateway processes it using the first transmission rule. According to the first transmission rule, the edge gateway organizes the collected full power data locally and then uploads it to the cloud server through the preset first transmission channel. This allows control over the frequency or amount of data uploads while ensuring that the cloud can obtain complete operating data, thereby avoiding resource consumption caused by high-frequency full transmissions.

[0032] Specifically, for example, in practice, the preset cycle here can be 5 to 30 minutes, and the compression processing can use the LZ4 lightweight compression algorithm. Based on this configuration, compared with the method of uploading the entire data every minute, the number of data transmissions and the size of data packets per unit time can be significantly reduced in actual engineering projects. Continuing with the example of a deployment scenario with 1000 power distribution rooms, 2000 data points per power distribution room, and using a 4G network, if the method of uploading every minute is used, each edge gateway will generate 1440 transmissions per day; while with a 5-minute cycle, the number of transmissions drops to 288, and with a 30-minute cycle, it drops to 48. The significant reduction in transmission frequency directly leads to a multiple reduction in 4G traffic. At the same time, the LZ4 compression algorithm has the characteristics of fast compression speed and low computing power consumption, which can adapt to the limited computing resources of the edge gateway, and the compression ratio can reach more than 3:1, further reducing the actual amount of data transmitted. The combination of these two features effectively controls the bandwidth usage and traffic costs of the 4G network, providing an economical and feasible transmission solution for remote monitoring of large-scale power distribution room clusters.

[0033] In some embodiments, the second transmission rule includes: when a preset event is detected, generating core event information and uploading the core event information to a cloud server through a preset second transmission channel; obtaining auxiliary data and media data associated with the preset event and uploading the auxiliary data and media data to the cloud server accordingly through preset third and fourth transmission channels.

[0034] In other words, the second transmission rule is triggered when the edge gateway identifies a preset event locally. These preset events include, but are not limited to, changes in the value of power data points (such as a switch changing from open to closed) or exceeding limits (such as voltage or current exceeding preset thresholds). After identifying a preset event, the edge gateway first generates core event information. This core event information is a concise data packet describing the basic attributes of the event, including the time of the event, the event type, the device identifier where the event occurred, and key values ​​at the time of the event. That is, the core event information includes at least the event type, device number, event threshold, and current measured value. This core event information is then uploaded to the cloud server in real time through the preset second transmission channel, ensuring that the cloud can obtain the basic information about the event with minimal latency.

[0035] After the core event information is uploaded, the edge gateway further acquires auxiliary data and media data associated with the event. Auxiliary data refers to operational data related to the event device within a certain period before and after the event. Specifically, based on the needs of actual engineering scenarios, the auxiliary data here includes change characteristic data or sampled data of the event device within a preset time period; media data includes video clips or comparison images before and after the event. For example, auxiliary data includes the changing characteristics of the event-affected device within the first 10 minutes or historical data sampled minute by minute. This data can reflect the trend changes before and after the event, providing contextual information for root cause analysis in the cloud. Media data consists of video clips one minute before and one minute after the event, or comparative images before and after the event. This data can provide maintenance personnel with intuitive on-site judgment.

[0036] In the above process, the core event information is first uploaded through the second transmission channel, while auxiliary data and media data are uploaded to the cloud server through the preset third and fourth transmission channels, respectively. This layered transmission approach—transmitting the core event information first to ensure real-time performance, and then transmitting auxiliary data and media data as needed—avoids network congestion caused by transmitting large amounts of data when an alarm occurs, and provides complete multi-dimensional information support for subsequent event analysis, thereby improving the efficiency and accuracy of event processing.

[0037] Furthermore, based on the independent local autonomy built on the edge gateway side, when a preset event is detected, the edge gateway can generate local alarm information without relying on the cloud. In some embodiments, the alarm content can be pushed to the mobile phones of maintenance personnel via the SMS module built into the gateway.

[0038] Furthermore, the inventors discovered in practical engineering experience that network outages are an unavoidable and common occurrence in the deployment of power distribution room monitoring systems. In reality, power distribution rooms are often located in urban corners, underground spaces, or remote areas, and 4G networks are commonly used. However, 4G signal quality varies greatly, and factors such as operator network maintenance and signal obstruction often lead to fluctuating network connections or even prolonged outages. If traditional solutions place all business logic in the cloud, the edge gateway will lose its connection to the cloud during network outages. It will be unable to upload real-time data or obtain cloud-based judgment instructions, resulting in local maintenance personnel not receiving timely alerts. Critical data generated during network outages also faces the risk of loss due to the lack of a reliable continuation mechanism, severely impacting the business continuity and data integrity of the monitoring system.

[0039] To address the aforementioned issues, this application introduces a third transmission rule into the differentiated transmission strategy. This rule involves processing data cached during network outages using a specific transmission rule. Specifically, the differentiated transmission strategy further includes: applying a third transmission rule to data cached during network outages. This third transmission rule includes: when a network outage is detected, caching the acquired power data and event-related data to local storage; when network recovery is detected, reading the cached data from local storage and uploading it to the cloud server in batches through a preset fifth transmission channel; and clearing the uploaded cached data after receiving confirmation from the cloud server.

[0040] In actual implementation, through specific configurations, the edge gateway monitors the network status in real time. When a network interruption is detected, it immediately triggers a local autonomous mode, continuously caching acquired power data and event-related data to local storage. In this mode, the edge gateway continues to perform local data acquisition and event identification, ensuring that real-time monitoring of the power distribution room is not interrupted due to network outages. When network recovery is detected, the edge gateway automatically reads the cached data from local storage and uploads it to the cloud server in batches through a preset fifth transmission channel. After receiving confirmation from the cloud server, it clears the uploaded cached data to avoid duplicate uploads and storage space overflow.

[0041] To ensure efficient uploading of cached data and accurate cloud restoration during network outages, this application introduces a resume batch number and a current batch sequence number into the data packets uploaded in batches. Specifically, in some embodiments, the data packets uploaded in batches include a resume batch number and a current batch sequence number, enabling the cloud server to reassemble the received data based on these two numbers.

[0042] Specifically, when initiating a resume transmission, the edge gateway generates a batch number to identify the current resume task. For each batch of data packets, the message body carries the current batch number and the total number of batches. After receiving the data from each batch, the cloud server reassembles the data in an orderly manner according to the resume batch number and the current batch number. After all batches of data have been received, the cloud server synchronizes the data timestamps with the edge side to ensure data consistency.

[0043] Through the design of the aforementioned third transmission rule, this application achieves local autonomy and breakpoint resumption in network interruption scenarios, effectively solving the problems of easy data loss and low recovery efficiency in existing technologies, ensuring the continuity of power distribution room monitoring services, and providing reliable data transmission guarantee for remote monitoring of large-scale power distribution room clusters.

[0044] The technical solution proposed in this application addresses the specific needs of the application's technical scenario by employing a differentiated transmission strategy and an edge autonomy mechanism to help reduce data transmission load. Specifically, this application uses an edge gateway to classify and process power data locally and uploads the data to the cloud server according to a preset differentiated transmission strategy. This allows data in different states to be processed using differentiated transmission rules, thus avoiding high-frequency, indiscriminate uploading of all data during normal operation and effectively reducing network traffic consumption and bandwidth pressure. Simultaneously, because the edge gateway completes data classification and processing locally, event-triggered data can be initially identified and processed locally, reducing real-time dependence on the cloud server. A third transmission rule is used for data during network outages, enabling the edge gateway to cache data locally during network interruptions and upload it after network recovery, ensuring data integrity and business continuity under network fluctuation scenarios. Furthermore, the differentiated transmission strategy uses different transmission rules for normal operation data and event-triggered data, allowing the two types of data to be transmitted according to their respective characteristics, providing a foundation for subsequent data processing by the cloud server.

[0045] To facilitate understanding of the technical solution of this application, the technical solution of this application will be further described and explained below in conjunction with further technical implementation details.

[0046] In the technical solution of this application, the "transmission channel" mentioned in the above embodiments refers to the logical path between the edge gateway and the cloud server used to transmit different types of data. Its specific implementation can be flexibly selected according to the actual deployment scenario and communication protocol. For example, a RESTful interface based on the HTTP / HTTPS protocol can be used to distinguish data types through different URL paths; a bidirectional communication channel based on the WebSocket protocol can be used to achieve data splitting through different message identifiers; or an application-layer message format based on a custom TCP protocol can be used to classify data through the type field in the message header, etc.

[0047] Considering the comprehensive requirements of power distribution room monitoring scenarios for network stability, transmission reliability, and resource consumption, some embodiments of this application use the MQTT protocol as the basis for the transmission channel in actual engineering implementations. As those skilled in the art know, the MQTT protocol is a lightweight publish / subscribe message transmission protocol with characteristics such as low protocol overhead, support for unstable networks, and provision of multiple quality of service levels, making it well-suited to the actual characteristics of public wireless communication networks.

[0048] Based on this, the first transmission channel, the second transmission channel, the third transmission channel, the fourth transmission channel, and the fifth transmission channel in this application are predefined message topics based on the MQTT protocol; wherein, the edge gateway uses its own unique code as the MQTT client identifier to access the cloud MQTT server, the message topics are divided into multiple secondary topics according to business type, and the edge gateway realizes the uploading of different types of data by publishing messages to the corresponding secondary topics.

[0049] In practical implementation, multiple message topics can be predefined based on the MQTT protocol, with each topic corresponding to a type of business data. The edge gateway uses its unique code as an MQTT client identifier to connect to the cloud MQTT server, publishing different types of data to their corresponding message topics. The cloud server subscribes to these topics to achieve data classification, reception, and processing. This MQTT topic-based transmission channel implementation ensures logical isolation between different types of data at the transmission layer and fully utilizes the reliable transmission capabilities of the MQTT protocol under unstable networks, providing an efficient and reliable data transmission foundation for remote monitoring of large-scale power distribution room clusters.

[0050] For example, in practice, all MQTT message topics use "power / monitor" as the root topic, and are then divided into five sub-topics based on business type. All edge gateways use the same topic for similar data, thus avoiding topic fragmentation and reducing the complexity of cloud subscription and maintenance. The five sub-topics and their uses are as follows: The first category is a fixed-period full-volume compressed data topic, with the path "power / monitor / full_data", which is used to upload the full power data packaged and compressed by the edge gateway according to a preset period. The MQTT service quality level is set to 1 to balance transmission reliability and efficiency. The second category is the data transmission topic for network outage recovery and resume transmission, with the path "power / monitor / resume_data". It is used to upload locally cached data during network outages, with the service quality level set to 1, supporting reliable transmission in batches. The third category is the core event information topic, with the path "power / monitor / alarm / core". It is used to upload core information of events such as displacement or exceeding limits. The service quality level is set to 2 to ensure that the event information is delivered only once and is not lost, thus ensuring the reliable transmission of alarms. The fourth category is event-related auxiliary data topics, with the path "power / monitor / alarm / attach", used to upload auxiliary data related to the event (such as the change characteristics data or sampling data of the event device in the past 10 minutes), and the service quality level is set to 1; The fifth category is event-related media data topics, with the path "power / monitor / alarm / media", used to upload video clips or comparison images before and after the event, and the service quality level is set to 1; Through the above-mentioned standardized theme design, this application achieves logical isolation of different types of data at the transmission layer. Combined with the differentiated configuration of service quality level, it ensures reliable transmission of key data while taking into account transmission efficiency, and fully adapts to the diverse data transmission needs of the 4G monitoring scenario in the power distribution room.

[0051] On the other hand, regarding data format design, this application encodes the uploaded data using a binary serialization format. All message bodies follow fixed encoding rules, with the message body header containing at least two fixed fields: a unique edge gateway code and a timestamp. That is, the edge gateway uploads processed data to the cloud server via a public wireless communication network, including: the edge gateway using its own unique code as an identifier to access the cloud server; the uploaded data being encoded using a binary serialization format, with the data packet header containing at least the edge gateway's unique code and a timestamp. It should be noted that this application uses a binary serialization format for data encoding. Compared to commonly used text-based serialization formats (such as JSON and XML) in existing technologies, binary serialization has advantages such as smaller data size, lower parsing overhead, and higher transmission efficiency. It can effectively adapt to the bandwidth constraints of public wireless communication networks, and is particularly suitable for large-scale monitoring scenarios involving thousands of data points per gateway in power distribution rooms.

[0052] As a specific implementation method, all MQTT transmitted data follows the following encoding rules: The message header always includes three fields: the first field is a unique edge gateway code, a fixed-length binary string used to identify the data source; the second field is a UTC millisecond-level timestamp, a uint64 type, precisely recording the time of data collection or event occurrence; and the third field is the total length of the data body, a uint32 type, indicating the number of bytes in the subsequent data content, facilitating data boundary identification and integrity verification in the cloud. This three-field design allows the cloud server to quickly complete source identification, timing, and length verification upon receiving the message without traversing the entire data packet, significantly reducing processing latency in basic parsing.

[0053] For different data types, the message body carries corresponding extended fields after a fixed header. In some implementation scenarios, the specific configuration is as follows: For fixed-period full-volume compressed data, the message body also includes a compression algorithm identifier, the total number of original data points, and the compressed binary stream. The compression algorithm identifier, using the uint8 type, indicates the compression algorithm used (e.g., 0 represents LZ4 compression). The total number of original data points, using the uint16 type, records the number of data points included in this upload. The compressed binary stream is a byte stream type, carrying the full data content processed by the compression algorithm.

[0054] For core alarm information, the message body also includes alarm type, device number, data point number, alarm threshold or baseline value, current measured value, alarm level, and local SMS push status. The alarm type uses a uint8 type to distinguish the nature of the event (e.g., 0 for a displacement event, 1 for an out-of-limit event); the device number and data point number are used to accurately locate the device and measurement point where the event occurred; the alarm threshold or baseline value uses a float64 type to record the preset threshold that triggers the alarm; the current measured value uses a float64 type to record the actual measured value when the alarm is triggered; the alarm level uses a uint8 type to indicate the severity of the event; and the local SMS push status uses a uint8 type to record the execution status of local push notifications from the edge gateway.

[0055] For interrupted data transmission, the message body also includes the network interruption start timestamp, network interruption recovery timestamp, transmission batch number, current batch sequence number, total batch quantity, number of data points in this batch, and compressed binary stream. The network interruption start timestamp and network interruption recovery timestamp are both of type uint64, used to identify the start and end times of the network interruption; the transmission batch number is of type uint32, used to identify the unique number of this transmission task; the current batch sequence number and total batch quantity are both of type uint16, used to indicate the position of the current data packet in the entire transmission task, facilitating data splicing and reconstruction in the cloud; the number of data points in this batch is of type uint16, recording the number of data points contained in the current batch; the compressed binary stream is a byte stream type, carrying the compressed data content.

[0056] Furthermore, by using the unique code of the edge gateway to identify the data source, the cloud server can identify the power distribution room to which the data belongs without parsing the data body content. The timestamp uses UTC millisecond precision to identify the precise moment of data collection or event occurrence, facilitating data alignment and timing processing in the cloud. The design of fixed header fields allows the cloud server to extract basic information with minimal parsing overhead after receiving the message, reducing data processing latency. As mentioned above, after the header fields, the message body carries corresponding data content according to different business types, such as periodic full data being carried as a compressed binary data stream. This unified binary encoding format has higher transmission efficiency and lower parsing overhead compared to text formats (such as JSON and XML), effectively adapting to the bandwidth constraints of public wireless networks.

[0057] This application employs an MQTT-based topic-based transmission channel implementation, combined with a unified binary encoding format, offering the following technical advantages: First, the hierarchical topic design achieves logical isolation of different data types at the transmission layer. Specifically, the layered data transmission mechanism for event triggering configures core event information, auxiliary data, and media data into independent secondary topics. This ensures that critical information necessary for event response is prioritized, while auxiliary and media data are transmitted on demand. This guarantees real-time alarm performance while maximizing the efficient use of network resources, providing customized channel support for the collaborative transmission and accurate analysis of multi-dimensional data in power distribution room monitoring scenarios, facilitating targeted processing in the cloud. Second, the unified binary encoding format and fixed header field design reduce the complexity of cloud parsing and shorten the end-to-end latency from data transmission to processing. Third, communication between the edge gateway and the cloud server follows the standard MQTT protocol, exhibiting good compatibility and scalability, facilitating large-scale system deployment and maintenance. The above design fully adapts to the differentiated data transmission needs in 4G monitoring scenarios in power distribution rooms, providing an efficient and reliable communication foundation for remote monitoring of large-scale power distribution room clusters.

[0058] Furthermore, on the cloud side, this application further constructs a cloud-edge collaborative iteration mechanism. After receiving and processing the data uploaded by each edge gateway, the cloud server can perform statistical analysis on massive amounts of data from a global perspective, and optimize the identification thresholds for events such as displacement and limit violations based on the operational data of the entire power distribution room. Then, the cloud sends the optimized configuration information to the corresponding edge gateway through downlink topics. After receiving the configuration information, the edge gateway updates its local differentiated transmission strategy (such as adjusting the periodic full upload period, adjusting the collection range of event-related data, etc.) or event identification threshold (such as modifying the trigger threshold of limit violation alarms, adjusting the sensitivity of displacement judgment, etc.) according to the content of the configuration information.

[0059] That is, the power distribution room monitoring data transmission method of this application includes receiving configuration information from the cloud server at the edge gateway and updating the local differentiated transmission strategy or event recognition threshold according to the configuration information.

[0060] In this way, compared to a single edge gateway relying solely on local data for judgment, the cloud aggregates historical operational data and event records from all power distribution rooms, enabling the discovery of more universal patterns and thus deriving better judgment parameters. In this way, the cloud can leverage its global data advantage to continuously optimize the operational strategies on the edge side, while the edge side achieves rapid response through local execution. Actual monitoring systems using this technical solution can sustainably improve the accuracy of event identification and operational efficiency over long-term operation, effectively addressing the problems of insufficient cloud-edge collaboration and limited intelligence levels in existing technologies.

[0061] In one embodiment, such as Figure 2 As shown, this application also proposes a power distribution room monitoring data transmission system, which includes: an end-side acquisition layer deployed in the power distribution room for acquiring raw power parameter data; an edge processing layer including edge gateways deployed in each power distribution room, the edge gateways being used to execute the power distribution room monitoring data transmission methods as described in the above embodiments; a transmission link layer providing network connectivity based on a public wireless communication network for data transmission between the edge processing layer and the cloud service layer; and a cloud service layer deployed in the cloud for receiving and processing data uploaded by the edge gateways.

[0062] Furthermore, in some embodiments, the edge processing layer also integrates an edge AI inference module for performing event recognition locally; the cloud service layer also integrates an event auxiliary discrimination module for verifying events by combining auxiliary data and media data uploaded from the edge side, and then sending the optimized event recognition threshold to the edge gateway based on global data.

[0063] In other words, this application also provides a power distribution room monitoring data transmission system for performing the steps in the foregoing method embodiments. The system includes an end-side acquisition layer, an edge processing layer, a transmission link layer, and a cloud service layer.

[0064] For example, the end-side acquisition layer is deployed in 1,000 power distribution rooms, each containing up to 2,000 power data acquisition points, including various acquisition devices such as voltage transformers, current transformers, temperature and humidity sensors, and switch quantity acquisition modules, for collecting raw power parameter data.

[0065] The edge processing layer includes edge gateways deployed in each power distribution room, such as one edge gateway per power distribution room. Each edge gateway integrates an MQTT client, a position change detection module, an over-limit detection module, an LZ4 compression module, a local caching module, and an SMS alarm module, and uses its own unique code as an MQTT client identifier to access the cloud server. This edge processing layer is used to realize edge autonomy and data preprocessing, including acquiring locally collected power data, classifying and processing the data according to differentiated transmission strategies, uploading data to the cloud server, and performing local autonomy and breakpoint resume in the event of network interruption.

[0066] The transmission link layer uses a 4G wireless network for data transmission between the edge processing layer and the cloud service layer. Preferably, the edge gateway can also support redundant switching between 4G and wired networks and be configured with data transmission rate limiting to adapt to 4G bandwidth constraints.

[0067] The cloud service layer is deployed in the cloud and includes an MQTT server, a data decoding and verification module, an alarm auxiliary judgment module, a global monitoring and analysis module, a time-series database, and an object storage module. The MQTT server receives MQTT messages uploaded from all edge gateways and distributes data according to preset topics. The data decoding and verification module decodes and verifies the integrity of binary encoded data. The alarm auxiliary judgment module combines auxiliary data and media data uploaded from the edge side to verify the authenticity of alarms and perform root cause analysis. The global monitoring and analysis module performs statistical analysis and strategy optimization based on all distribution room data. The time-series database stores all power data for historical backtracking and trend analysis. The object storage module stores unstructured media data such as videos and images.

[0068] The edge processing layer also integrates an edge AI inference module for real-time local displacement and limit violation detection, further reducing reliance on cloud servers. Meanwhile, the cloud service layer's alarm assistance module, upon receiving alarm assistance data and media data uploaded from the edge, combines historical data from the entire power distribution room to perform authenticity verification and root cause analysis of alarm events, and optimizes the edge's discrimination threshold based on the global analysis results. The optimized threshold is then distributed to the corresponding edge gateway via MQTT downlink topics. Upon receiving the threshold, the edge gateway updates its local configuration, forming a cloud-edge collaborative iterative closed loop.

[0069] Through the above system architecture, this application realizes a complete closed loop of end-side data acquisition, edge processing, and cloud-based collaboration, providing complete hardware and software support for 4G remote monitoring of large-scale power distribution room clusters.

[0070] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0071] It should be noted that in the description of this application, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means at least two.

[0072] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0073] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for data transmission in a power distribution room monitoring system, applied to an edge gateway, characterized in that: include: Acquire locally collected power data; According to a preset differentiated transmission strategy, the power data is classified and processed locally, and the processed data is uploaded to the cloud server through a public wireless communication network so that the cloud server can receive and process the data. The differentiated transmission strategy includes: using a first transmission rule for data under normal operating conditions, using a second transmission rule for data related to events, and using a third transmission rule for data cached during network outages.

2. The power distribution room monitoring data transmission method according to claim 1, wherein, The second transmission rule includes: When a preset event is detected, core event information is generated and uploaded to the cloud server through a preset second transmission channel; The auxiliary data and media data associated with the preset event are acquired, and the auxiliary data and media data are uploaded to the cloud server through the preset third and fourth transmission channels.

3. The power distribution room monitoring data transmission method according to claim 2, wherein, The core information of the event includes at least the event type, device number, event threshold, and current measured value; the auxiliary data includes the change characteristic data or sampling data of the event device within a preset time period; and the media data includes video clips or comparison images before and after the event.

4. The power distribution room monitoring data transmission method according to claim 2, wherein, The first transmission rule includes: compressing the acquired full power data according to a preset period to generate a periodic full data packet; and uploading the periodic full data packet to a cloud server through a preset first transmission channel. The third transmission rule includes: when a network interruption is detected, the acquired power data and event-related data are cached in local storage; when the network is restored, the cached data is read from the local storage and uploaded to the cloud server in batches through the preset fifth transmission channel; and the uploaded cached data is cleared after receiving confirmation from the cloud server.

5. The power distribution room monitoring data transmission method according to claim 4, wherein, The data packets uploaded in batches include a resume batch number and a current batch sequence number, so that the cloud server can reassemble the received data based on the resume batch number and the current batch sequence number.

6. The power distribution room monitoring data transmission method according to claim 4, wherein, The first, second, third, fourth, and fifth transmission channels are predefined message topics based on the MQTT protocol. The edge gateway uses its own unique code as an MQTT client identifier to access the cloud MQTT server. The message topics are divided into multiple secondary topics according to business types. The edge gateway uploads different types of data by publishing messages to the corresponding secondary topics.

7. The power distribution room monitoring data transmission method according to claim 1, wherein, The process of uploading processed data to a cloud server via a public wireless communication network includes: The edge gateway uses its own unique code as an identifier to access the cloud server; The uploaded data is encoded in binary serialization format, and the header of the data packet contains at least the edge gateway's unique code and a timestamp.

8. The power distribution room monitoring data transmission method according to claim 1, wherein, The public wireless communication network includes 4G networks.

9. A power distribution room monitoring data transmission system, characterized in that, include: The end-side acquisition layer, deployed in the power distribution room, is used to collect raw power parameter data; An edge processing layer includes edge gateways deployed in each power distribution room, the edge gateways being used to execute the power distribution room monitoring data transmission method as described in any one of claims 1 to 8; The transmission link layer provides network connectivity based on a public wireless communication network for data transmission between the edge processing layer and the cloud service layer. The cloud service layer, deployed in the cloud, is used to receive and process data uploaded by the edge gateway.

10. The power distribution room monitoring data transmission system according to claim 9, wherein, The edge processing layer also integrates an edge AI inference module for performing event recognition locally; the cloud service layer also integrates an event auxiliary discrimination module for verifying events by combining auxiliary data and media data uploaded from the edge side, and then sending the event recognition threshold to the edge gateway after optimizing the event recognition threshold based on global data.

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